Why Traditional Audience Research Is Dead

The #1 rule of marketing is simple: Know your audience. But for marketing and ecommerce teams, knowing who they’re trying to reach has been more of an art than an exact science. 

Brands have historically lacked real-time insight into audience preferences. Instead, they’ve settled for relying on a combination of gut instinct as well as quantitative and qualitative research to form a picture of their target audience. 

While traditional tactics have long been a staple of marketing, their slow nature has increasingly become outdated and ineffective in today's fast-paced digital landscape. These methods are reactive instead of predictive, with results rolling in long after products have entered the market or campaigns have elapsed. It amounts to wasted resources and untapped potential. 

AI has the potential to change all of that. But before we look to the future of audience research, let’s take a look at the past. 

The Limitations of Traditional Audience Research Methods

Businesses get audience research through a variety of methods, including:

• Surveys and polls
• Focus groups
• Customer feedback
• Web analytics 
• Sales and conversion data 
• Field testing 
• A/B testing 
• And more

Teams use a combination of these tactics to develop a deeper understanding of their target audience, which informs marketing and ecommerce strategies and improves their chances of success. But there are some notable drawbacks to these methods that have hampered teams for years.

• Time constraints: Collecting feedback is time consuming, and much of this feedback only comes after a product launch. By the time insights makes their way to ecommerce, marketing, and creative teams, many of the findings may be outdated and companies have already wasted resources. 
• Limited sample sizes: There is always a risk of bias when gathering audience information. For example, customer survey data only reflects information about those who opted in or may already know about the brand, thus missing critical feedback from the target audience you’re missing. 
• Lack of context: Many of these methods tell you how a product is doing with a small sample of people, but not how it’s faring against its competitors. For example, A/B testing can be very valuable in learning which of two marketing tactics get the most attention, but it doesn’t reveal any industry benchmarks to determine if the resulting winner would earn attention against the field. 
• Skills gap: Teams are strapped for resources. Even those able to invest the time and money into audience research may lack the team members or knowledge required to analyze that data and turn it into actionable insights. 
• Costs: Whether it’s compensating focus group participants or hiring employees to make sense of analytics, traditional research methods can be cost prohibitive. And the costs only grow as businesses scale. 

Digital has transformed all aspects of marketing and ecommerce, and audience research is no different. In today’s age of increased competition—where there are thousands of other brands just a quick online search away—brands cannot sit back and rely on outdated methods. 

Bringing AI-Based Insights to Imagery 

Artificial intelligence sidesteps many of the obstacles that come along with traditional research while helping teams make data-driven decisions, be efficient with media spend, and create more consumer impact. Forward-thinking companies are turning to AI-based predictive analytics, which provide more accurate and comprehensive insights into audience behavior and preferences. 

Brands and retailers today need to create a smarter, systematic way of creating and maintaining optimized content. When it comes to brand visuals, that means:

• Test any content idea you or your designers have on their mind with AI.
• Iterate on those ideas, rapidly. Play with different colors, backgrounds, treatments, angles, etc. and instantly see how that influences the audience. 
• Use AI to determine the best few ideas using data that shows they are optimized and competitively advantaged.
• Put your final set of optimized content into the market.
• Use analytics to keep a pulse on how those content assets are performing relative to competitor content.

When a consumer views you in the context of a competitor, you will now naturally stand out. Those are the moments where your content can and will win the sale for you. Imagine this happens with every single image of every product in your catalog and every campaign your brand runs. 

Traditional audience research methods may not be completely dead. But they need a jolt to survive—and AI provides it. Predictive image analytics is revolutionizing how brands and retailers collect and apply audience insights.